ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.123
Optimal Design of Sparse Reconfigurable Arrays Based on Multiple Measurement Vector Model
Abstract— In this paper, an innovative approach is presented for the optimal design of sparse reconfigurable
antenna arrays. The synthesis of sparse arrays is recast as a simultaneous sparse approximation problem and
modeled with multiple measurement vector (MMV). A multiple response extension of the sparse Bayesian
learning (SBL) named M-SBL is exploited to solve the above optimization problem. The common element
positions and individual element excitations for multiple radiation patterns are obtained simultaneously by
searching out multiple sparse weight vectors. The proposed approach can dynamically reconfigure different
patterns with a higher matching accuracy and array sparseness. A representative experiment is provided to
validate the effectiveness and advantages of the proposed method.
Index Terms— multiple measurement vector, simultaneous sparse approximation, pattern reconfigurable antenna, sparse antenna arrays, array synthesis
Haiou Shen, Buhong Wang
Information and Navigation College, Air Force Engineering University, Xi’an, China
Cite: Haiou Shen, Buhong Wang, "Optimal Design of Sparse Reconfigurable Arrays Based on Multiple Measurement Vector Model," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 706-710, Beijing, 25-27 June, 2017.